İşlevsiz Hipofiz Nöroendokrin Tümörlerinin Tedavisinde Yeniden Konumlandırılmış Bir İlaç Adayı Olarak Maprotilin: Ağ Tabanlı Omik Odaklı Bir Yaklaşım

Abstract

İşlevsiz hipofiz nöroendokrin tümörleri, sinsi bir ilerlemeye sahip olduğundan sadece klinik açıdan değil aynı zamanda hastalar için de büyük bir zorluk teşkil etmektedir. Zahmetli çalışmalar ve son teknoloji yöntemler bu hastalık hakkındaki bilgimizi geliştirse de, şu anda hastalığın kesin tedavisi için onaylanmış bir yöntem yoktur. Bu çalışma, yüksek verimli RNA dizileme analizlerinden elde edilen transkriptom verilerini kullanarak, NF-PitNET'te normalden farklı ifade desenleri gösteren, anlatımı önemli ölçüde değişmiş genleri belirlemeyi amaçlamıştır. Hastalığın patogenezi için gerekli unsurları ortaya çıkarmak amacıyla protein, transkripsiyon faktörü ve mikroRNA düzeylerindeki biyolojik ağlar oluşturulmuş ve topolojik olarak analiz edilmiştir. Bu analizler sonucunda AGO2, BCL2L2, BIRC5, BRCC3, CDC42, CUL3, E2F2, ESR1, ESR2, GIGYF1, JUN, KRAS, MDM2, NFKB1, PLEKHA4, RELA, RNF40, ve ZNF460 ağ bileşenleri biyolojik ağların merkezi elemanları olarak bulunmuş ve NF-PitNET'lerin sistem biyobelirteçleri olarak önerilmiştir. Merkezi elemanlarını tedavi hedefi olarak kulanan bir ilaç yeniden konumlandırma çalışması yapılmış ve valdekoksib, penfluridol, maprotilin hcl, mitoksantron, vorinostat, homoharringtonin, noretinodrel, strofint oktahidrat, bufalin ve digoksin dahil olmak üzere yeniden konumlandırılmış ilaç adayları ortaya çıkarılmıştır. Moleküler kenetlenme (docking) yöntemiyle yapılan in silico analizler, maprotilinin merkezî unsurlarla etkileşiminde, mevcut inhibitörlere kıyasla daha yüksek bağlanma afinitesi gösterdiğini ortaya koymuştur. Bu bulgular doğrultusunda, maprotilin, NF-PitNET tedavisinde umut vadeden yeniden konumlandırılmış bir terapötik ajan olarak önerilmektedir.

Keywords

• transkriptomik
• RNA dizileme
• biyolojik ağlar
• ilaç repozisyonu
• hipofiz nöroendokrin tümörler

Project Number

Not Available.

Maprotiline as a Repositioned Drug Candidate for the Treatment of Non-Functioning Pituitary Neuroendocrine Tumors: a Network-Based Omics-Oriented Approach

Abstract

Non-functioning pituitary neuroendocrine tumors pose a great challenge not only for the clinic but also for patients since it has an insidious progression. Even though effortful studies and state-of-the-art techniques are improving our knowledge about this disease, no therapeutic modality is currently approved for the treatment. This study aimed to determine significantly altered genes that showed aberrantly expressed patterns in NF-PitNET using high-throughput RNA-sequencing transcriptome data. To uncover essential elements in disease pathogenesis, biological networks in protein, transcription factor, and microRNA levels were constructed and topologically analyzed. by drug prioritization for NF-PitNETs via a repositioning approach. The hub elemets of AGO2, BCL2L2, BIRC5, BRCC3, CDC42, CUL3, E2F2, ESR1, ESR2, GIGYF1, JUN, KRAS, MDM2, NFKB1, PLEKHA4, RELA, RNF40, and ZNF460 were proposed as systems biomarkers of NF-PitNET. A signature-based drug repositioning using hub elements as treatment targets unraveled repositioned drug candidates including valdecoxib, penfluridol, maprotiline, mitoxantrone, vorinostat, homoharringtonine, norethynodrel, strophantine octahydrate, bufalin, and digoxin. The efficiency of maprotiline was confirmed in silico via molecular docking and resulted in higher binding affinities with hub elements compared to their inhibitors. Maprotiline was proposed as a promising repositioned therapeutic for the management of NF-PitNETs.

Keywords

• transcriptomics
• RNA-seq
• biological networks
• drug repositioning
• non-functioning pituitary neuroendocrine tumors

Supporting Institution

Not available.

Project Number

Not Available.

Ethical Statement

The author declares that the datasets used in this study belonged to previously published studies, therefore no ethical statement is required.

Thanks

The author would like to thank BioRender.com, Figure 1 was created using trial version of the web-based tool.

References

1. Asa, S.L., Casar-Borota, O., Chanson, P., Delgrange, E., Earls, P., Ezzat, S., et al. (2017). From pituitary adenoma to pituitary neuroendocrine tumor (PitNET): an International Pituitary Pathology Club proposal. Endocrine-Related Cancer, 24, C5–8.
2. Ostrom, Q.T., Gittleman, H., Truitt, G., Boscia, A., Kruchko, C., Barnholtz-Sloan, J.S. (2018). CBTRUS statistical report: primary brain and other central nervous system tumors diagnosed in the United States in 2011–2015. Neuro-Oncology, 20, iv1–86.
3. Daly, A.F., Beckers, A. (2020). The Epidemiology of Pituitary Adenomas. Endocrinology and Metabolism Clinics of North America, 49, 347–55.
4. Vargas, G., Gonzalez, B., Ramirez, C., Ferreira, A., Espinosa, E., Mendoza, V., et al. (2015). Clinical characteristics and treatment outcome of 485 patients with nonfunctioning pituitary macroadenomas. International Journal of Endocrinology, 2015.
5. Penn, D.L., Burke, W.T., Laws, E.R. (2018). Management of non-functioning pituitary adenomas: surgery. Pituitary, 21, 145–53.
6. Ilie, M.D., Raverot, G. (2020). Treatment options for gonadotroph tumors: current state and perspectives. Journal of Clinical Endocrinology & Metabolism, 105, e3507–18.
7. ClinicalTrials.gov [Internet]. National Library of Medicine (US); (accessed 2025 April 10).
8. Aydin, B., Arga, K.Y. (2019). Co-expression network analysis elucidated a core module in association with prognosis of non-functioning non-invasive human pituitary adenoma. Frontiers in Endocrinology (Lausanne), 10, 361.

9. Gadelha, M.R., Kasuki, L., Dénes, J., Trivellin, G., Korbonits, M. (2013). MicroRNAs: Suggested role in pituitary adenoma pathogenesis. Journal of Endocrinological Investigation, 36, 889–95.
10. Gossing, W., Frohme, M., Radke, L. (2020). Biomarkers for Liquid Biopsies of Pituitary Neuroendocrine Tumors. Biomedicines, 8. https://doi.org/10.3390/biomedicines8060148.
11. Aydin, B., Caliskan, A., Arga, K.Y. (2021). Overview of omics biomarkers in pituitary neuroendocrine tumors to design future diagnosis and treatment strategies. EPMA Journal, 12, 383–401.
12. Aydin, B., Yildirim, E., Erdogan, O., Arga, K.Y., Yilmaz, B.K., Bozkurt, S.U., et al. (2022). Past, Present, and Future of Therapies for Pituitary Neuroendocrine Tumors: Need for Omics and Drug Repositioning Guidance. Omi A Journal of Integrative Biology, 26, 115–29.
13. Beylerli, O., Beeraka, N.M., Gareev, I., Pavlov, V., Yang, G., Liang, Y., et al. (2020). MiRNAs as Noninvasive Biomarkers and Therapeutic Agents of Pituitary Adenomas. International Journal of Molecular Sciences, 21, 7287.
14. Zhang, Z., Zhou, L., Xie, N., Nice, E.C., Zhang, T., Cui, Y., et al. (2020). Overcoming cancer therapeutic bottleneck by drug repurposing. Signal Transduction and Targeted Therapy, 5, 113.
15. Barrett, T., Wilhite, S.E., Ledoux, P., Evangelista, C., Kim, I.F., Tomashevsky, M., et al. (2012). NCBI GEO: archive for functional genomics data sets—update. Nucleic Acids Research, 41, D991–5.
16. Kober, P., Boresowicz, J., Rusetska, N., Maksymowicz, M., Paziewska, A., Dąbrowska, M., et al. (2019). The role of aberrant DNA methylation in misregulation of gene expression in gonadotroph nonfunctioning pituitary tumors. Cancers (Basel), 11, 1650.
17. DeLellis, R.A. (2004). Pathology and genetics of tumours of endocrine organs. Vol. 8. IARC.
18. Knosp, E., Steiner, E., Kitz, K., Matula, C. (1993). Pituitary adenomas with invasion of the cavernous sinus space: a magnetic resonance imaging classification compared with surgical findings. Neurosurgery, 33, 610–8.
19. Love, M.I., Huber, W., Anders, S. (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology, 15, 1–21.
20. García-Moreno, A., López-Domínguez, R., Ramirez-Mena, A., Pascual-Montano, A., Aparicio-Puerta, E., Hackenberg, M., et al. (2021). GeneCodis 4: Expanding the modular enrichment analysis to regulatory elements. BioRxiv, 2004–21.
21. Zhou, Y., Zhou, B., Pache, L., Chang, M., Khodabakhshi, A.H., Tanaseichuk, O., et al. (2019). Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nature Communications, 10, 1523. https://doi.org/10.1038/s41467-019-09234-6.
22. Kanehisa, M., Araki, M., Goto, S., Hattori, M., Hirakawa, M., Itoh, M., et al. (2007). KEGG for linking genomes to life and the environment. Nucleic Acids Research, 36, D480–4.
23. The Gene Ontology Resource: 20 years and still GOing strong. (2019). Nucleic Acids Research, 47, D330–8. https://doi.org/10.1093/nar/gky1055.
24. Fabregat, A., Sidiropoulos, K., Garapati, P., Gillespie, M., Hausmann, K., Haw, R., et al. (2016). The Reactome pathway Knowledgebase. Nucleic Acids Research, 44, D481-7. https://doi.org/10.1093/nar/gkv1351.
25. Oughtred, R., Stark, C., Breitkreutz, B.-J., Rust, J., Boucher, L., Chang, C., et al. (2019). The BioGRID interaction database: 2019 update. Nucleic Acids Research, 47, D529–41.
26. Huang, H.-Y., Lin, Y.-C.D., Li, J., Huang, K.-Y., Shrestha, S., Hong, H.-C., et al. (2020). miRTarBase 2020: updates to the experimentally validated microRNA–target interaction database. Nucleic Acids Research, 48, D148–54.
27. Han, H., Cho, J.-W., Lee, S., Yun, A., Kim, H., Bae, D., et al. (2018). TRRUST v2: an expanded reference database of human and mouse transcriptional regulatory interactions. Nucleic Acids Research, 46, D380–6.
28. Saito, R., Smoot, M.E., Ono, K., Ruscheinski, J., Wang, P.-L., Lotia, S., et al. (2012). A travel guide to Cytoscape plugins. Nature Methods, 9, 1069–76.
29. Chin, C.-H., Chen, S.-H., Wu, H.-H., Ho, C.-W., Ko, M.-T., Lin, C.-Y. (2014). cytoHubba: identifying hub objects and sub-networks from complex interactome. BMC Systems Biology, 8 Suppl 4, S11. https://doi.org/10.1186/1752-0509-8-S4-S11.
30. Campillos, M., Kuhn, M., Gavin, A.-C., Jensen, L.J., Bork, P. (2008). Drug target identification using side-effect similarity. Science, 321, 263–6.
31. Duan, Q., Reid, S.P., Clark, N.R., Wang, Z., Fernandez, N.F., Rouillard, A.D., et al. (2016). L1000CDS2: LINCS L1000 characteristic direction signatures search engine. NPJ Systems Biology and Applications, 2, 16015. https://doi.org/10.1038/npjsba.2016.15.
32. Kim, S., Chen, J., Cheng, T., Gindulyte, A., He, J., He, S., et al. (2019). PubChem 2019 update: improved access to chemical data. Nucleic Acids Research, 47, D1102–9. https://doi.org/10.1093/nar/gky1033.
33. Davis, A.P., Grondin, C.J., Johnson, R.J., Sciaky, D., Wiegers, J., Wiegers, T.C., et al. (2021). Comparative Toxicogenomics Database (CTD): update 2021. Nucleic Acids Research, 49, D1138–43. https://doi.org/10.1093/nar/gkaa891.
34. Berman, H., Henrick, K., Nakamura, H. (2003). Announcing the worldwide protein data bank. Nature Structural & Molecular Biology, 10, 980.
35. McGreig, J.E., Uri, H., Antczak, M., Sternberg, M.J.E., Michaelis, M., Wass, M.N. (2022). 3DLigandSite: structure-based prediction of protein–ligand binding sites. Nucleic Acids Research, 50, W13–20.
36. Gutmanas, A., Alhroub, Y., Battle, G.M., Berrisford, J.M., Bochet, E., Conroy, M.J., et al. (2014). PDBe: protein data bank in Europe. Nucleic Acids Research, 42, D285–91.
37. Trott, O., Olson, A.J. (2010). AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. Journal of Computational Chemistry, 31, 455–61.
38. Aydin, B., Arslan, S., Bayraklı, F., Karademir, B., Arga, K.Y. (2022). MicroRNA-Mediated Drug Repurposing Unveiled Potential Candidate Drugs for Prolactinoma Treatment. Neuroendocrinology, 112, 161–73.
39. Campana, C., Nista, F., Castelletti, L., Caputo, M., Lavezzi, E., Marzullo, P., et al. (2022). Clinical and radiological presentation of parasellar ectopic pituitary adenomas: case series and systematic review of the literature. Journal of Endocrinological Investigation, 45, 1465–81.
40. Islam, T., Rahman, M.R., Aydin, B., Beklen, H., Arga, K.Y., Shahjaman, M. (2020). Integrative transcriptomics analysis of lung epithelial cells and identification of repurposable drug candidates for COVID-19. European Journal of Pharmacology, 887. https://doi.org/10.1016/j.ejphar.2020.173594.
41. Di Somma, C., Scarano, E., de Alteriis, G., Barrea, L., Riccio, E., Arianna, R., et al. (2021). Is there any gender difference in epidemiology, clinical presentation and co-morbidities of non-functioning pituitary adenomas? A prospective survey of a National Referral Center and review of the literature. Journal of Endocrinological Investigation, 44, 957–68.
42. Van Leeuwaarde, R.S., Pieterman, C.R.C., May, A.M., Dekkers, O.M., Van der Horst-Schrivers, A.N., Hermus, A.R., et al. (2021). Health-related quality of life in patients with multiple endocrine neoplasia type 1. Neuroendocrinology, 111, 288–96.
43. Rafiee, L., Hajhashemi, V., & Javanmard, S.H. (2019). Maprotiline inhibits COX2 and iNOS gene expression in lipopolysaccharide-stimulated U937 macrophages and carrageenan-induced paw edema in rats. Central European Journal of Biology, 44(1), 15-22.
44. Liang, L., Li, Y., Jiao, Y., Zhang, C., Shao, M., Jiang, H., Wu, Z., Chen, H., Guo, J., Jia, H., & Zhao, T. (2024). Maprotiline prompts an antitumour effect by inhibiting PD-L1 expression in mice with melanoma. Current Topics in Molecular Pharmacology, 17(1), e18761429259562. https://doi.org/10.2174/18761429259562230925055749
45. Zhou, Y., Zhang, Y., Wang, Y., Xu, Y., Wang, J., & Zhang, Y. (2024). Network medicine-based strategy identifies maprotiline as a repurposable drug by inhibiting PD-L1 expression via targeting SPOP in cancer. Advanced Science, 11(1), 210285. https://doi.org/10.1002/advs.202410285
46. Zhu, Y., Bu, S., Curbo, S., & Johansson, M. (2023). Antidepressants as autophagy modulators for cancer therapy. Molecules, 28(22), 7594. https://doi.org/10.3390/molecules28227594.
47. Wishart, D.S., Feunang, Y.D., Guo, A.C., Lo, E.J., Marcu, A., Grant, J.R., et al. (2018). DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Research, 46(D1), D1074-D1082. https://doi.org/10.1093/nar/gkx1037.
48. Kästner, U., Hecht, K., Klinger, W. (1980). Distribution of maprotiline in brain regions of the rat. Neuropharmacology, 19(9), 797-801. https://doi.org/10.1016/0028-3908(80)90106-8.
49. Pardridge, W.M. (2005). The blood–brain barrier: bottleneck in brain drug development. NeuroRx, 2(1), 3-14. https://doi.org/10.1602/neurorx.2.1.3.